This work was performed in collaboration with Dr. AE Sumner of the Clinical Endocrinology Branch, NIDDK, Dr. RN Bergman of the USC Medical School and Dr. CC Chow of LBM, NIDDK. [unreadable] [unreadable] The purpose of this investigation was to quantitatively assess FFA response to insulin. We postulated that a mathematical model transforming a time-course of plasma insulin measurements into a time-course of FFA measurements would be described by parameters that would serve as quantitative measures of the regulation of lipolysis by insulin action. We considered several different mathematical models of varying complexity and mechanisms of action. We discriminated between these models on the basis of standard principles of model comparison: (1) Goodness of fit, (2) Parameter space required to describe the model, (3) Identifiability of parameters, and (4) Biological relevance. [unreadable] [unreadable] The best performing model balancing fit to data and model complexity has insulin action through a remote adipose compartment that is different from the glucose remote compartment of the glucose minimal model. Physiologically, it may not be surprising that the remote adipose compartment from which insulin affects FFA dynamics is different from the remote glucose compartment from which insulin modulates glucose levels. The pathways through which insulin regulates glucose involves the insulin receptor on the cell surface and ultimately glucose transporters. In contrast insulin regulates lipolysis by initiating a chain of events that leads to inhibition of lipolysis, by promoting the dephosphorylation of both hormone sensitive lipase and the protein perilipin.[unreadable] [unreadable] For the kinetics of model II-2, there is a linear clearance rate (only dependent on FFA concentration) and a nonlinear suppression of FFA appearance by remote acting insulin. We found that a first order Hill function was sufficient to represent the relevant pathways. This may suggest that there is a single first order process within these pathways that is rate limiting. The Hill function could be compared to the FFA Ra dependence on insulin as found in insulin clamp experiments, where it was found that the data could be fit to a power law. A Hill function could be an alternative parametric form for a fit to this data and is thus consistent with the clamp results.[unreadable] [unreadable] One aspect of the time course of FFA concentration is that the final level is almost always higher than the initial level. The model is able to match this aspect of the data although it does not insert a specific biophysical mechanism to account for this effect. Instead, the model assumes that the initial value of FFA is a free parameter. At 360 minutes, insulin concentration has returned to basal levels and FFA attains a level that can be higher than the initial value. The elevation in FFA levels at 360 minutes may occur for several reasons, including the rebound after exposure to the initial bolus of glucose, the effect of hypoglycemia, counter regulatory hormones, or diurnal variation. [unreadable] [unreadable] [unreadable] Previous studies indicate that there may be a maximally suppressible level of FFA plasma appearance. This would be manifested as the insulin independent lipolysis rate in the Type 3 models. However, our model evaluation showed that adding this parameter did not improve the fit to the data enough to warrant its inclusion. The reason may be the wide variability in the measured baseline FFA levels. Additional data may be required to resolve the insulin independent rate. [unreadable] [unreadable] We focused exclusively on the action of insulin on FFA levels and we found that our models adequately fit to the data to produce a sensitivity index SFI. As a result we did not explore the possibility of direct action of glucose on FFA levels. The action of glucose is indirectly accounted for through its effect on insulin. There is experimental evidence supporting our decision. Our philosophy of employing a minimal model also meant that we did not incorporate the direct effects of catecholamines, corticosteroids and glucagon. Presumably, these hormones contribute to the variability not accounted for by the model. As FFA promote resistance to insulins ability to regulate glucose, this model could potentially lead to a greater insight into our understanding for why some individuals with obesity are resistant to insulins effect on glucose and others are sensitive. The SFI could also potentially guide the selection of patients that are most likely to respond to agents which increase insulin sensitivity in adipose tissues such as thiazolidinediones. The significant negative correlation between SFI and fat mass is presumptive evidence that SFI will ultimately be of value in guiding therapy with insulin sensitizers.[unreadable] [unreadable] We recognize that the model we developed must be tested in other populations, be tested for reproducibility and undergo rigorous validation. One means of providing validation is to perform an IM-FSIGT and an insulin clamp experiment with a tracer such as labeled palmitate on the same subject and compare our model parameters to those derived from the clamp. The clamp experiments can give an estimate of the dependence on FFA rate of appearance on insulin and the rate of FFA clearance, which can be compared to the equivalent quantities derived from our model. The value of the current model is that it is a FFA minimal model that appears to explain most of the FFA levels during the IM-FSIGT. These results are being prepared for publication now to allow the models to be subject to wider study and interrogation.